LoreDirick / dsc-postgrad-reviewing-presentation-content-exercises

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Project Presentations - Exercises

Introduction:

Before you work on your own materials, let's revisit the messy presentation materials to examine the impact of reviewing project content. This lesson is a quiz that will require you to review the presentation through the lens of refining the presentation content. Take time to think through the quiz questions before reviewing the answers in Part 2!

Learning goals:

  • review content of an example presentation
  • identify areas where the sample presentation could improve

Part 1: Quiz

Question 1: If the target audience is an average person trying to sell their home, what information included in the presentation is not aligned to that audience? (can be more than one)

A. How closely the model is able to predict house sale prices, in dollars

B. The R-Squared score of the final model

C. Contact details

D. A list of all the models that were tried over the course of the project

Question 2: Assuming the target audience is an average person trying to sell their home, how would you answer the audience persona questions?

Question Answers
1. Who will be receiving your presentation and, if relevant, what is their role in the company?
2. What is their background: technical or non-technical?
3. How will your presentation affect their work or a decision they need to make?
4. What is their attention span?
5. What is the goal of the presentation?

Question 3: Which presentation title is more engaging?

A. House Price Predictions

B. Sell Your Home Faster!

C. Capstone Project

Question 4: Which description of the business problem do you think would be the most engaging to the target audience (an average person trying to sell their home)?

A. Predictions of the sale prices of houses are really useful but they are hard to calculate accurately

B. This Kaggle competition asks competitors to predict house sale prices

C. Selling a home can be a torturous process - without an accurate estimate of how much your home will sell for, it's hard to settle on a realistic starting sale price

The next three questions will use the following presentation outline, along with key information that will be contained on each slide:

  1. Overview of presentation
  2. Data: Where the data came from, and an in-depth look at all of the data used in the model
  3. Data Preparation: How the data transformed between the raw data and what was used in the model
  4. Solution: Overview of the model and how it solves this problem better than other approaches (or baseline)
  5. Modeling Process: Overview of how key considerations within the data made you choose the preferred model over other options
  6. Results and Evaluation: How well the model succeeds at solving this problem, with a brief discussion of limitations
  7. Recommendations: How to make the findings from the model actionable
  8. Next Steps: Future work that could be done to improve or better integrate this solution
  9. Wrap Up: Conclusion slide to reiterate the results and recommendations

Question 5: Which two key bits of information are missing?

Question 6: Which slides could we remove?

Question 7: Which slide might have too much information?


You have finished the quiz! Make sure you have answered each question before scrolling into Part 2.


Part 2: Answers

Question 1:

B and D

Question 2:

  1. Audience: consumer of the analysis (end customer)
  2. Background: Non-technical
  3. How will they use it? Affect how they appraise how much their home will likely sell for
  4. Short attention span
  5. Goal: For them to buy access to your analysis

Question 3:

B

Question 4:

C

Question 5:

Presentation outline is missing:

  • Hook: An introduction to the business context and the business problem
  • Contact details: how an interested customer could reach you

Question 6:

We could remove:

  • Data Preparation
  • Modeling Process

Both are too technical and too detailed to be relevant to our target audience.

Question 7:

Data Analysis slide probably goes into too much detail - a brief overview of how the data is relevant to the problem is probably more appropriate.

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